Abstract
The paper addresses an experiment in detecting metaphorical usage of adjectives and nouns in Polish data. First, we describe the data developed for the experiment. The corpus consists of 1833 excerpts containing adjective-noun phrases which can have both metaphorical and literal senses. Annotators assign literal or metaphorical senses to all adjectives and nouns in the data. Then, we describe two methods for literal/metaphorical sense classification. The first method uses Bi-LSTM neural network architecture and word embeddings of both token- and character-level. We examine the influence of adversarial training and perform analysis by part-of-speech. The second method uses the BERT token-level classifier. On our relatively small data, the LSTM based approach gives significantly better results and achieves an F1 score equal to 0.81.
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Acknowledgments
This work was supported by the Polish National Science Centre project Compositional distributional semantic models for identification, discrimination and disambiguation of senses in Polish texts (2014/15/B/ST6/05186).
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Wawer, A., Marciniak, M., Mykowiecka, A. (2022). Neural Nets in Detecting Word Level Metaphors in Polish. In: Vetulani, Z., Paroubek, P., Kubis, M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2019. Lecture Notes in Computer Science(), vol 13212. Springer, Cham. https://doi.org/10.1007/978-3-031-05328-3_18
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